This invention relates generally to the inspection of components and, more particularly, to a method and apparatus for performing nondestructive testing of fabricated components.
Where nondestructive evaluation of a workpiece or component is required, ultrasonic inspection techniques are used in many applications. One application of such ultrasonic inspection is in the inspection of gas turbine engine components such as rotors and blades, for example. Such components are typically formed from forging or casting a material with desired metallurgical properties. In the production of aerospace rotating components, the entire volume of the finished component is required to be inspected ultrasonically.
More specifically, there are many inspection or sensing applications where data are collected and stored for analysis. Certain types of applications are designed to detect signals from the ultrasonic probes or sensors in conditions where the background noise amplitude in the data varies greatly, for example, a variation of 6-12 dB, over the area of interest. In some applications signal features other than amplitude such as morphology or frequency can be used to help differentiate it from the background noise. However in some applications the only method to discriminate the signal from the background noise is relative amplitude or signal-to-noise ratio (SNR).
One example of such an application is the ultrasonic inspection of titanium forgings for material anomalies. This process creates two-dimensional or three-dimensional images with highly varying background noise caused by the underlying microstructures. However, the material anomalies for which the inspection is looking, e.g. hard-alpha, stress cracks, strain induced porosity, and foreign material, may have a morphology or frequency response which is similar to that produced by the microstructure. As a result, the inspector ultimately accepts or rejects the component being tested by detecting the presence of defect indications in these images in terms of their SNR.
For example, during the inspection process, the operator analyzes the ultrasound data to identify potential SNRs that may indicate a flaw. More specifically, the operator first locates a potential indication by manually searching each image for a suspect signal. Once the operator has identified a suspect signal, the operator manually draws a bounding box around the suspect signal. To complete the SNR calculation, the operator also determines a homogenous area of background noise surrounding the suspect signal. Statistics such as mean, max, and standard deviation are then applied to the data signal and noise areas to calculate the SNR for the indication. While this technique is acceptable for images having a homogenous background noise, this technique is less effective when the image includes variable background noise which obscures the homogenous noise thus making the selection of the signal by the operator both difficult and subject to operator interpretation.